




'''simple docstring'''


import numpy





class  __lowercase    :







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       input_array

     # Random initial weights are assigned where first argument is the
     # number of nodes in previous layer and second argument is the
     # number of nodes in the next layer.

     # Random initial weights are assigned.
     # self.input_array.shape[1] is used to represent number of nodes in input layer.
     # First hidden layer consists of 4 nodes.
     __a								=       numpy.random.rand(
         self.input_array.shape[1]   ,				4   )

     # Random initial values for the first hidden layer.
     # First hidden layer has 4 nodes.
     # Second hidden layer has 3 nodes.
     __a								=       numpy.random.rand(
         4   ,				3   )

     # Random initial values for the second hidden layer.
     # Second hidden layer has 3 nodes.
     # Output layer has 1 node.
     __a								=       numpy.random.rand(3   ,				1   )

     # Real output values provided.
     __a								=       output_array

     # Predicted output values by the neural network.
     # Predicted_output array initially consists of zeroes.
     __a								=       numpy.zeros(output_array.shape   )







   def 		UpperCamelCase__      (						self   )					->     numpy.ndarray:
     __a								=       sigmoid(
         numpy.dot(self.input_array   ,				self.input_layer_and_first_hidden_layer_weights   )   )

     # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
     # connecting the first hidden set of nodes with the second hidden set of nodes.
     __a								=       sigmoid(
         numpy.dot(
             self.layer_between_input_and_first_hidden_layer   ,				self.first_hidden_layer_and_second_hidden_layer_weights   ,				)   )

     # layer_between_second_hidden_layer_and_output is the layer connecting
     # second hidden layer with the output node.
     __a								=       sigmoid(
         numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer   ,				self.second_hidden_layer_and_output_layer_weights   ,				)   )

     return self.layer_between_second_hidden_layer_and_output







   def 		UpperCamelCase__      (						self   )					->     None:
     __a								=       numpy.dot(
         self.layer_between_first_hidden_layer_and_second_hidden_layer.T   ,				2
         * (self.output_array - self.predicted_output)
         * sigmoid_derivative(self.predicted_output   )   ,				)
     __a								=       numpy.dot(
         self.layer_between_input_and_first_hidden_layer.T   ,				numpy.dot(
             2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output   )   ,				self.second_hidden_layer_and_output_layer_weights.T   ,				)
         * sigmoid_derivative(
             self.layer_between_first_hidden_layer_and_second_hidden_layer   )   ,				)
     __a								=       numpy.dot(
         self.input_array.T   ,				numpy.dot(
             numpy.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output   )   ,				self.second_hidden_layer_and_output_layer_weights.T   ,				)
             * sigmoid_derivative(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer   )   ,				self.first_hidden_layer_and_second_hidden_layer_weights.T   ,				)
         * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer   )   ,				)

     self.input_layer_and_first_hidden_layer_weights += (
         updated_input_layer_and_first_hidden_layer_weights
     )
     self.first_hidden_layer_and_second_hidden_layer_weights += (
         updated_first_hidden_layer_and_second_hidden_layer_weights
     )
     self.second_hidden_layer_and_output_layer_weights += (
         updated_second_hidden_layer_and_output_layer_weights
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     for iteration in range(1   ,				iterations + 1   ):
       __a								=       self.feedforward()
       self.back_propagation()
       if give_loss:
         __a								=       numpy.mean(numpy.square(output - self.feedforward()   )   )
         print(f"Iteration {iteration} Loss: {loss}"   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     int:
     __a								=       input_arr

     __a								=       sigmoid(
         numpy.dot(self.array   ,				self.input_layer_and_first_hidden_layer_weights   )   )

     __a								=       sigmoid(
         numpy.dot(
             self.layer_between_input_and_first_hidden_layer   ,				self.first_hidden_layer_and_second_hidden_layer_weights   ,				)   )

     __a								=       sigmoid(
         numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer   ,				self.second_hidden_layer_and_output_layer_weights   ,				)   )

     return int(self.layer_between_second_hidden_layer_and_output > 0.6   )


def     SCREAMING_SNAKE_CASE							(   a_						:				numpy.ndarray      ):
  return 1 / (1 + numpy.exp(-value      ))


def     SCREAMING_SNAKE_CASE							(   a_						:				numpy.ndarray      ):
  return (value) * (1 - (value))


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       numpy.array(
      (
          [0, 0, 0],
          [0, 0, 1],
          [0, 1, 0],
          [0, 1, 1],
          [1, 0, 0],
          [1, 0, 1],
          [1, 1, 0],
          [1, 1, 1],
      )					,       dtype=numpy.floataa					,       )

  # True output values for the given input values.
  __a								=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])					,       dtype=numpy.floataa      )

  # Calling neural network class.
  __a								=       TwoHiddenLayerNeuralNetwork(
      input_array=a_					,       output_array=a_      )

  # Calling training function.
  # Set give_loss to True if you want to see loss in every iteration.
  neural_network.train(output=a_					,       iterations=10					,       give_loss=a_      )

  return neural_network.predict(numpy.array(([1, 1, 1])					,       dtype=numpy.floataa      )      )


if __name__ == "__main__":
  example()









'''simple docstring'''


import math


def     SCREAMING_SNAKE_CASE							(   a_						:				float					,       a_						:				float      ):

  if initial_intensity < 0:
    raise ValueError('The value of intensity cannot be negative'      )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 360:
    raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'      )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(a_      )      ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



